HEATGait: Hop-Extracted Adjacency Technique in Graph Convolution based Gait Recognition
This work addresses a specific issue in model-based gait recognition for biometric authentication, representing an incremental improvement.
The paper tackled biased weighting in multi-scale graph convolution for gait recognition by introducing HEATGait, which uses a hop-extraction technique to improve feature extraction, achieving state-of-the-art performance on the CASIA-B dataset.
Biometric authentication using gait has become a promising field due to its unobtrusive nature. Recent approaches in model-based gait recognition techniques utilize spatio-temporal graphs for the elegant extraction of gait features. However, existing methods often rely on multi-scale operators for extracting long-range relationships among joints resulting in biased weighting. In this paper, we present HEATGait, a gait recognition system that improves the existing multi-scale graph convolution by efficient hop-extraction technique to alleviate the issue. Combined with preprocessing and augmentation techniques, we propose a powerful feature extractor that utilizes ResGCN to achieve state-of-the-art performance in model-based gait recognition on the CASIA-B gait dataset.